library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
all10x <- readRDS('output/10x-180504')
cannot open compressed file 'output/10x-180504', probable reason 'No such file or directory'Error in gzfile(file, "rb") : cannot open the connection
One cluster in the data contains cells from all samples
TSNEPlot(all10x, group.by='sample_name', pt.size=0.1)

Cluster 12
TSNEPlot(all10x, group.by='res.0.5', pt.size=0.1, do.label=T)

Marker genes
markers <- read.table('output/markergenes/180504/markers_10x-180504_res.0.5_negbinom', sep='\t', header=T)
cannot open file 'output/markergenes/180504/markers_10x-180504_res.0.5_negbinom': No such file or directoryError in file(file, "rt") : cannot open the connection
Positive markers
as.data.frame(mixture_pos)
Negative markers
as.data.frame(mixture_neg)
Top 10 positive markers
VlnPlot(all10x, features.plot=toupper(mixture_pos$gene[1:10]), group.by='res.0.5', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)

Top 10 negative markers
VlnPlot(all10x, features.plot=toupper(mixture_neg$gene[1:10]), group.by='res.0.5', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)

Plots
all10x@meta.data$mixture <- ifelse(all10x@meta.data$res.0.5==12, "mixture", "rest")
VlnPlot(all10x, features.plot=c('nGene', 'MALAT1', 'NEAT1', 'FN1', 'ITGB1', 'COL1A1', 'COL1A2', 'percent.mito'), group.by='mixture', point.size.use=-1, nCol=4)

Nr of cells
Sample composition in cluster 12.
cluster12 <- SubsetData(all10x, cells.use=rownames(all10x@meta.data)[which(all10x@meta.data$res.0.5 %in% 12)])
rotate_x <- function(data, column_to_plot, labels_vec, rot_angle) {
plt <- barplot(data[[column_to_plot]], col='steelblue', xaxt="n")
text(plt, par("usr")[3], labels = labels_vec, srt = rot_angle, adj = c(1.1,1.1), xpd = TRUE, cex=1)
}
rotate_x((cluster12@meta.data %>% count(sample_name))[,2], 'n', as.vector(unlist((cluster12@meta.data %>% count(sample_name))[,1])), 45)

scmap results

Add line breaks for legend


Stressed cell analysis
length(stress_genes)
[1] 140
stress_genes
[1] Actg1 Btg1 Cxcl1 Dnajb4 Errfi1 H3f3b Hspb1 Irf1 Klf6 Mir22hg
[11] Nfkbia Pcf11 Pxdc1 Sdc4 Srf Tpm3 Usp2 Gadd45g Ankrd1 Btg2
[21] Cyr61 Dusp1 Fam132b Hipk3 Hsph1 Irf8 Klf9 Mt1 Nfkbiz Pde4b
[31] Rap1b Serpine1 Srsf5 Tppp3 Wac Hspe1 Arid5a Ccnl1 Dcn Dusp8
[41] Fos Hsp90aa1 Id3 Itpkc Litaf Mt2 Nop58 Per1 Rassf1 Skil
[51] Srsf7 Tra2a Zc3h12a Ier5 Atf3 Ccrn4l Ddx3x Egr1 Fosb Hsp90ab1
[61] Idi1 Jun Lmna Myadm Nppc Phlda1 Rhob Slc10a6 Stat3 Tra2b
[71] Zfand5 Kcne4 Atf4 Cebpb Ddx5 Egr2 Fosl2 Hspa1a Ier2 Junb
[81] Maff Myc Nr4a1 Pnp Rhoh Slc38a2 Tagln2 Trib1 Zfp36 Bag3
[91] Cebpd Des Eif1 Gadd45a Hspa1b Ier3 Jund Mafk Myd88 Odc1
[101] Pnrc1 Ripk1 Slc41a1 Tiparp Tubb4b Zfp36l1 Bhlhe40 Cebpg Dnaja1 Eif5
[111] Gcc1 Hspa5 Ifrd1 Klf2 Mcl1 Nckap5l Osgin1 Ppp1cc Sat1 Socs3
[121] Tnfaip3 Tubb6 Zfp36l2 Brd2 Csrnp1 Dnajb1 Erf Gem Hspa8 Il6
[131] Klf4 Midn Ncoa7 Oxnad1 Ppp1r15a Sbno2 Sqstm1 Tnfaip6 Ubc Zyx
140 Levels: Actg1 Ankrd1 Arid5a Atf3 Atf4 Bag3 Bhlhe40 Brd2 Btg1 Btg2 Ccnl1 Ccrn4l ... Zyx
Cluster 12 is the mixture cluster. Check markers MALAT1 and NEAT1 to be sure:

Are any of the stress genes in the DE genes for the mixture cluster?
3 of the 140 genes were found in the positive markers for the mixture cluster, 54 in the negative numbers. Similar numbers were found for the other clusters in the data. These results indicate that the mixture cluster does not consist of stressed cells.
Figures for report
get_violin_plot <- function(x){
return(VlnPlot(all10x, features.plot=c(x), point.size.use=-1, group.by='mixture', size.title.use=14, remove.legend=T) + theme(axis.title.x=element_blank()))
}
fig2_first_row <- plot_grid(
TSNEPlot(all10x, group.by='sample_name', pt.size=0.1),
TSNEPlot(all10x, group.by='predicted_labels_fat_breaks', pt.size=0.1) + theme(legend.key.height=unit(1, "cm")),
labels=c('a', 'b'), rel_widths=c(48/100, 52/100)
)


fig2_violin <- plot_grid(
get_violin_plot('nGene'),
get_violin_plot('MALAT1'),
get_violin_plot('NEAT1'),
get_violin_plot('FN1'),
get_violin_plot('ITGB1'),
get_violin_plot('COL1A1'),
get_violin_plot('COL1A2'),
get_violin_plot('percent.mito'),
labels=c('d', 'e', 'f', 'g', 'h', 'i', 'j', 'k'), nrow=2
)
fig2_barplot <- plot_grid(ggplot(data=cluster12@meta.data %>% count(sample_name), aes(x=sample_name, y=n)) +
geom_bar(stat="identity", position='dodge') +
scale_y_continuous(expand=c(0,0)) +
theme(axis.line.y=element_blank(), axis.ticks.y=element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank()) +
coord_flip(), labels=c('c'))
fig2_second_row <- plot_grid(fig2_barplot, fig2_violin, rel_widths=c(2/8, 6/8))
fig2 <- plot_grid(
fig2_first_row,
fig2_second_row,
nrow=2
)
fig2

#save_plot("plots/180504_mixture.pdf", fig2, base_width=12, base_height=9)
# get_violin_plot <- function(x){
# return(VlnPlot(all10x, features.plot=c(x), point.size.use=-1, group.by='mixture', size.title.use=14, remove.legend=T) + theme(axis.title.x=element_blank()))
# }
#
# fig2_first_row <- plot_grid(
# TSNEPlot(all10x, group.by='sample_name', pt.size=0.1) + theme(legend.position=c(0.1, 0.5), legend.key = element_blank(), legend.background= element_rect(fill=alpha('white', 0.8), size=0.5, linetype='solid', colour=alpha('black', 0.3))),
# DimPlot(all10x, reduction.use='tsne', cells.highlight = rownames(all10x@meta.data)[all10x@meta.data$res.0.5 == 12], cols.highlight='blue', cols.use='gray', pt.size=0.1),
# labels=c('a', 'b')
# )
#
# fig2_violin <- plot_grid(
# get_violin_plot('nGene'),
# get_violin_plot('MALAT1'),
# get_violin_plot('NEAT1'),
# get_violin_plot('FN1'),
# get_violin_plot('ITGB1'),
# get_violin_plot('COL1A1'),
# get_violin_plot('COL1A2'),
# get_violin_plot('percent.mito'),
# labels=c('d', 'e', 'f', 'g', 'h', 'i', 'j', 'k'), nrow=2
# )
#
# fig2_barplot <- plot_grid(ggplot(data=cluster12@meta.data %>% count(sample_name), aes(x=sample_name, y=n)) +
# geom_bar(stat="identity", position='dodge') +
# scale_y_continuous(expand=c(0,0)) +
# theme(axis.line.y=element_blank(), axis.ticks.y=element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank()) +
# coord_flip(), labels=c('c'))
#
# fig2_second_row <- plot_grid(fig2_barplot, fig2_violin, rel_widths=c(2/8, 6/8))
#
# fig2 <- plot_grid(
# fig2_first_row,
# fig2_second_row,
# nrow=2
# )
#
# fig2
#
# #save_plot("plots/180504_mixture.pdf", fig2, base_width=12, base_height=10)
write.table(stressed_cells, 'tables/10x-180504-stressed-cells-analysis.txt', sep='\t')
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(dplyr)
library(Seurat)
library(gProfileR)
```


```{r}
all10x <- readRDS('output/10x-180504')
```

One cluster in the data contains cells from all samples

```{r}
TSNEPlot(all10x, group.by='sample_name', pt.size=0.1)
```

Cluster 12

```{r}
TSNEPlot(all10x, group.by='res.0.5', pt.size=0.1, do.label=T)
```

#Marker genes

```{r}
markers <- read.table('output/markergenes/180504/markers_10x-180504_res.0.5_negbinom', sep='\t', header=T)
mixture <- markers[markers$cluster == 12,]
mixture <- mixture[mixture$p_val_adj < 0.05,]
mixture_pos <- mixture[order(-mixture$avg_logFC),]
mixture_neg <- mixture[order(mixture$avg_logFC),]
```

Positive markers 

```{r}
as.data.frame(mixture_pos)
```

Negative markers

```{r}
as.data.frame(mixture_neg)
```

Top 10 positive markers 

```{r fig1, fig.height = 15, fig.width = 10, fig.align = "center"}
VlnPlot(all10x, features.plot=toupper(mixture_pos$gene[1:10]), group.by='res.0.5', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)
```

Top 10 negative markers

```{r fig2, fig.height = 15, fig.width = 10, fig.align = "center"}
VlnPlot(all10x, features.plot=toupper(mixture_neg$gene[1:10]), group.by='res.0.5', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)
```

Plots

```{r fig4, fig.height = 5, fig.width = 9, fig.align = "center"}
all10x@meta.data$mixture <- ifelse(all10x@meta.data$res.0.5==12, "mixture", "rest")
VlnPlot(all10x, features.plot=c('nGene', 'MALAT1', 'NEAT1', 'FN1', 'ITGB1', 'COL1A1', 'COL1A2', 'percent.mito'), group.by='mixture', point.size.use=-1, nCol=4)
```


#Gene set enrichment

```{r}
gsea_revigo <- read.table('tables/Revigo_mixture-cluster_cleaned.txt', header=T, sep='\t')
gsea_revigo
```


#Nr of cells

Sample composition in cluster 12. 

```{r}
cluster12 <- SubsetData(all10x, cells.use=rownames(all10x@meta.data)[which(all10x@meta.data$res.0.5 %in% 12)])
rotate_x <- function(data, column_to_plot, labels_vec, rot_angle) {
     plt <- barplot(data[[column_to_plot]], col='steelblue', xaxt="n")
     text(plt, par("usr")[3], labels = labels_vec, srt = rot_angle, adj = c(1.1,1.1), xpd = TRUE, cex=1)
}
rotate_x((cluster12@meta.data %>% count(sample_name))[,2], 'n', as.vector(unlist((cluster12@meta.data %>% count(sample_name))[,1])), 45)
```

#scmap results

```{r}
TSNEPlot(all10x, group.by='predicted_labels_fat', pt.size=0.1)
```

Add line breaks for legend

```{r}
all10x@meta.data$predicted_labels_fat_breaks = as.vector(all10x@meta.data$predicted_labels_fat)
all10x@meta.data$predicted_labels_fat_breaks[which(all10x@meta.data$predicted_labels_fat == 'mesenchymal stem cell of adipose')] = 'mesenchymal\nstem cell\nof adipose'
all10x@meta.data$predicted_labels_fat_breaks[which(all10x@meta.data$predicted_labels_fat == 'smooth muscle cell')] = 'smooth\nmuscle cell'
```

```{r}
TSNEPlot(all10x, group.by='predicted_labels_fat_breaks', pt.size=0.1) + theme(legend.key.height=unit(1, "cm"))
```


#Stressed cell analysis

```{r}
stress_genes <- read.table('/raid5/projects/timshel/sc-arc_lira/src/data-genelists/171219-van_den_Brink2017-genes_affected_by_dissociation.csv', header=T) %>% pull(1)
length(stress_genes)
```

```{r}
stress_genes
```


```{r}
de_genes <- read.table('output/markergenes/180504/markers_10x-180504_res.0.5_negbinom', header=T)
de_genes <- de_genes[de_genes$p_val_adj < 0.05,]
```

Cluster 12 is the mixture cluster. Check markers MALAT1 and NEAT1 to be sure:

```{r fig7, fig.height = 3, fig.width = 10, fig.align = "center"}
VlnPlot(all10x, group.by='res.0.5', features.plot=c('MALAT1', 'NEAT1'), point.size.use =-1)
```

Are any of the stress genes in the DE genes for the mixture cluster?

```{r}
de_genes_pos <- de_genes[de_genes$avg_logFC > 0, ]
de_genes_neg <- de_genes[de_genes$avg_logFC < 0, ]

intersect_pos <- list()
intersect_neg <- list()
n_pos <- list()
n_neg <- list()
perc_pos <- list()
perc_neg <- list()

for (i in unique(de_genes$cluster)){
  c <- paste('cluster', i)

  genes_pos <- de_genes_pos[de_genes_pos$cluster == i, 'gene']
  genes_neg <- de_genes_neg[de_genes_neg$cluster == i, 'gene']
  
  intersect_pos[[c]] <- length(intersect(toupper(stress_genes), genes_pos))
  intersect_neg[[c]] <- length(intersect(toupper(stress_genes), genes_neg))
  n_pos[[c]] <- length(genes_pos)
  n_neg[[c]] <- length(genes_neg)
  perc_pos[[c]] <- (intersect_pos[[c]] / n_pos[[c]]) * 100
  perc_neg[[c]] <- (intersect_neg[[c]] / n_neg[[c]]) * 100
}

stressed_cells <- data.frame(
  shared.pos.genes=unlist(intersect_pos),
  shared.neg.genes=unlist(intersect_neg),
  n_pos=unlist(n_pos),
  n_neg=unlist(n_neg),
  perc_pos=unlist(perc_pos),
  perc_neg=unlist(perc_neg)
  )

stressed_cells <- stressed_cells[order(rownames(stressed_cells)),]
stressed_cells
```

3 of the 140 genes were found in the positive markers for the mixture cluster, 54 in the negative numbers. Similar numbers were found for the other clusters in the data. These results indicate that the mixture cluster does not consist of stressed cells. 

#Figures for report

```{r fig5, fig.height = 9, fig.width = 12, fig.align = "center"}
get_violin_plot <- function(x){
  return(VlnPlot(all10x, features.plot=c(x), point.size.use=-1, group.by='mixture', size.title.use=14, remove.legend=T) + theme(axis.title.x=element_blank()))
}

fig2_first_row <- plot_grid(
  TSNEPlot(all10x, group.by='sample_name', pt.size=0.1),
  TSNEPlot(all10x, group.by='predicted_labels_fat_breaks', pt.size=0.1) + theme(legend.key.height=unit(1, "cm")),
  labels=c('a', 'b'), rel_widths=c(48/100, 52/100)
)

fig2_violin <- plot_grid(
  get_violin_plot('nGene'),
  get_violin_plot('MALAT1'),
  get_violin_plot('NEAT1'),
  get_violin_plot('FN1'),
  get_violin_plot('ITGB1'),
  get_violin_plot('COL1A1'),
  get_violin_plot('COL1A2'),
  get_violin_plot('percent.mito'),
  labels=c('d', 'e', 'f', 'g', 'h', 'i', 'j', 'k'), nrow=2
)

fig2_barplot <- plot_grid(ggplot(data=cluster12@meta.data %>% count(sample_name), aes(x=sample_name, y=n)) +
  geom_bar(stat="identity", position='dodge') + 
  scale_y_continuous(expand=c(0,0)) +
  theme(axis.line.y=element_blank(), axis.ticks.y=element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank()) +
  coord_flip(), labels=c('c'))

fig2_second_row <- plot_grid(fig2_barplot, fig2_violin, rel_widths=c(2/8, 6/8))

fig2 <- plot_grid(
  fig2_first_row,
  fig2_second_row,
  nrow=2
)

fig2

save_plot("plots/180504_mixture.pdf", fig2, base_width=12, base_height=9)

```


```{r fig6, fig.height = 10, fig.width = 12, fig.align = "center"}
# get_violin_plot <- function(x){
#   return(VlnPlot(all10x, features.plot=c(x), point.size.use=-1, group.by='mixture', size.title.use=14, remove.legend=T) + theme(axis.title.x=element_blank()))
# }
# 
# fig2_first_row <- plot_grid(
#   TSNEPlot(all10x, group.by='sample_name', pt.size=0.1) + theme(legend.position=c(0.1, 0.5), legend.key = element_blank(), legend.background= element_rect(fill=alpha('white', 0.8), size=0.5, linetype='solid', colour=alpha('black', 0.3))),
#   DimPlot(all10x, reduction.use='tsne', cells.highlight = rownames(all10x@meta.data)[all10x@meta.data$res.0.5 == 12], cols.highlight='blue', cols.use='gray', pt.size=0.1),
#   labels=c('a', 'b')
# )
# 
# fig2_violin <- plot_grid(
#   get_violin_plot('nGene'),
#   get_violin_plot('MALAT1'),
#   get_violin_plot('NEAT1'),
#   get_violin_plot('FN1'),
#   get_violin_plot('ITGB1'),
#   get_violin_plot('COL1A1'),
#   get_violin_plot('COL1A2'),
#   get_violin_plot('percent.mito'),
#   labels=c('d', 'e', 'f', 'g', 'h', 'i', 'j', 'k'), nrow=2
# )
# 
# fig2_barplot <- plot_grid(ggplot(data=cluster12@meta.data %>% count(sample_name), aes(x=sample_name, y=n)) +
#   geom_bar(stat="identity", position='dodge') + 
#   scale_y_continuous(expand=c(0,0)) +
#   theme(axis.line.y=element_blank(), axis.ticks.y=element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank()) +
#   coord_flip(), labels=c('c'))
# 
# fig2_second_row <- plot_grid(fig2_barplot, fig2_violin, rel_widths=c(2/8, 6/8))
# 
# fig2 <- plot_grid(
#   fig2_first_row,
#   fig2_second_row,
#   nrow=2
# )
# 
# fig2
# 
# #save_plot("plots/180504_mixture.pdf", fig2, base_width=12, base_height=10)

```


```{r}
write.table(stressed_cells, 'tables/10x-180504-stressed-cells-analysis.txt', sep='\t')
```

